Oppositional biogeography-based optimization
Oppositional biogeography-based optimization
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DOI：
10.1109/icsmc.2009.5346043
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发表时间：
2009-10
期刊：
影响因子：
--
通讯作者：
Mehmet Ergezer;D. Simon;Dawei Du
中科院分区：
文献类型：
--
作者：
Mehmet Ergezer;D. Simon;Dawei Du
我们提出了一种新的变化，以地理为基础的优化（BBO），这是一种进化算法（EA）开发的全局优化。新算法采用基于对立的学习（OBL）以及BBO的迁移率来创建对立BBO（OB O）。此外，还介绍了一种新的对抗方法--拟反射法。准反射是基于相反的号码理论，我们从数学上证明，它具有最高的期望概率更接近所有OBL方法的问题解决方案。通过增加动态域缩放和加权反射，对对立算法进行了进一步的修改。模拟已被执行，以验证性能的准反对派，以及一个一维问题的数学分析。实证结果表明，在准反射的协助下，OB O显着优于BBO的成功率和找到最优解所需的适应度函数评估的数量。
We propose a novel variation to biogeography-based optimization (BBO), which is an evolutionary algorithm (EA) developed for global optimization. The new algorithm employs opposition-based learning (OBL) alongside BBO's migration rates to create oppositional BBO (OB O). Additionally, a new opposition method named quasi-reflection is introduced. Quasi-reflection is based on opposite numbers theory and we mathematically prove that it has the highest expected probability of being closer to the problem solution among all OBL methods. The oppositional algorithm is further revised by the addition of dynamic domain scaling and weighted reflection. Simulations have been performed to validate the performance of quasi-opposition as well as a mathematical analysis for a single-dimensional problem. Empirical results demonstrate that with the assistance of quasi-reflection, OB O significantly outperforms BBO in terms of success rate and the number of fitness function evaluations required to find an optimal solution.